# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""BERT finetuning runner."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import csv
import os

from bert import modeling
from bert import optimization
from bert import tokenization
from nets import text_rnn_crf

import tensorflow as tf

flags = tf.flags

FLAGS = flags.FLAGS

## Required parameters
flags.DEFINE_string(
    "data_dir", None,
    "The input data dir. Should contain the .tsv files (or other data files) "
    "for the task.")

flags.DEFINE_string(
    "bert_config_file", None,
    "The config json file corresponding to the pre-trained BERT model. "
    "This specifies the model architecture.")

flags.DEFINE_string("task_name", None, "The name of the task to train.")

flags.DEFINE_string("vocab_file", None,
                    "The vocabulary file that the BERT model was trained on.")

flags.DEFINE_string(
    "output_dir", None,
    "The output directory where the model checkpoints will be written.")

## Other parameters

flags.DEFINE_string(
    "init_checkpoint", None,
    "Initial checkpoint (usually from a pre-trained BERT model).")

flags.DEFINE_bool(
    "do_lower_case", True,
    "Whether to lower case the input text. Should be True for uncased "
    "models and False for cased models.")

flags.DEFINE_integer(
    "max_seq_length", 128,
    "The maximum total input sequence length after WordPiece tokenization. "
    "Sequences longer than this will be truncated, and sequences shorter "
    "than this will be padded.")

flags.DEFINE_bool("do_train", False, "Whether to run training.")

flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.")

flags.DEFINE_bool(
    "do_predict", False,
    "Whether to run the model in inference mode on the test set.")

flags.DEFINE_integer("train_batch_size", 16, "Total batch size for training.")

flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.")

flags.DEFINE_integer("predict_batch_size", 8, "Total batch size for predict.")

flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")

flags.DEFINE_float("num_train_epochs", 3.0,
                   "Total number of training epochs to perform.")

flags.DEFINE_float(
    "warmup_proportion", 0.1,
    "Proportion of training to perform linear learning rate warmup for. "
    "E.g., 0.1 = 10% of training.")

flags.DEFINE_integer("save_checkpoints_steps", 1000,
                     "How often to save the model checkpoint.")

flags.DEFINE_integer("iterations_per_loop", 1000,
                     "How many steps to make in each estimator call.")

flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")

tf.flags.DEFINE_string(
    "tpu_name", None,
    "The Cloud TPU to use for training. This should be either the name "
    "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
    "url.")

tf.flags.DEFINE_string(
    "tpu_zone", None,
    "[Optional] GCE zone where the Cloud TPU is located in. If not "
    "specified, we will attempt to automatically detect the GCE project from "
    "metadata.")

tf.flags.DEFINE_string(
    "gcp_project", None,
    "[Optional] Project name for the Cloud TPU-enabled project. If not "
    "specified, we will attempt to automatically detect the GCE project from "
    "metadata.")

tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.")

flags.DEFINE_integer(
    "num_tpu_cores", 8,
    "Only used if `use_tpu` is True. Total number of TPU cores to use.")


class InputExample(object):
    """A single training/test example for simple sequence classification."""

    def __init__(self, guid, text_a, text_b=None, label=None):
        """Constructs a InputExample.

        Args:
          guid: Unique id for the example.
          text_a: string. The untokenized text of the first sequence. For single
            sequence tasks, only this sequence must be specified.
          text_b: (Optional) string. The untokenized text of the second sequence.
            Only must be specified for sequence pair tasks.
          label: (Optional) string. The label of the example. This should be
            specified for train and dev examples, but not for test examples.
        """
        self.guid = guid
        self.text_a = text_a
        self.text_b = text_b
        self.label = label


class PaddingInputExample(object):
    """Fake example so the num input examples is a multiple of the batch size.

    When running eval/predict on the TPU, we need to pad the number of examples
    to be a multiple of the batch size, because the TPU requires a fixed batch
    size. The alternative is to drop the last batch, which is bad because it means
    the entire output data won't be generated.

    We use this class instead of `None` because treating `None` as padding
    battches could cause silent errors.
    """


class InputFeatures(object):
    """A single set of features of data."""

    def __init__(self,
                 input_ids,
                 input_mask,
                 segment_ids,
                 label_id,
                 is_real_example=True):
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids
        self.label_id = label_id
        self.is_real_example = is_real_example


class DataProcessor(object):
    """Base class for data converters for sequence classification data sets."""

    def get_train_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the train set."""
        raise NotImplementedError()

    def get_dev_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the dev set."""
        raise NotImplementedError()

    def get_test_examples(self, data_dir):
        """Gets a collection of `InputExample`s for prediction."""
        raise NotImplementedError()

    def get_labels(self):
        """Gets the list of labels for this data set."""
        raise NotImplementedError()

    @classmethod
    def _read_tsv(cls, input_file, quotechar=None, delimiter="\t"):
        """Reads a tab separated value file."""
        with tf.gfile.Open(input_file, "r") as f:
            reader = csv.reader(f, delimiter=delimiter, quotechar=quotechar)
            lines = []
            for line in reader:
                lines.append(line)
            return lines


class NERProcessor(DataProcessor):
    @classmethod
    def _read_ner_data(cls, file_path):
        idx = 0
        with open(file_path, 'r', encoding='utf-8') as reader:
            words = []
            labels = []
            for line in reader:
                # 1. 去除空格
                line = line.strip()
                # 2. 分割
                line = line.split(" ")
                # 3. 检查
                if len(line) == 2:
                    # 表示属于当前序列
                    word, label = line
                    words.append(word)
                    labels.append(label)
                elif len(line) == 1:
                    # 达到一个空行，表示一个序列文本完成
                    if len(words) > 0 and len(words) == len(labels):
                        yield idx, words, labels
                        idx += 1
                    words = []
                    labels = []
                else:
                    raise Exception("数据格式异常!!!")

    def get_train_examples(self, data_dir):
        # 1. 组成文件路径
        file_path = os.path.join(data_dir, "example.train")
        # 2. 数据遍历，形成Examples对象
        examples = []
        for idx, word, label in self._read_ner_data(file_path):
            example = InputExample(guid="train-{}".format(idx), text_a=word, label=label)
            examples.append(example)
        return examples

    def get_dev_examples(self, data_dir):
        # 1. 组成文件路径
        file_path = os.path.join(data_dir, "example.min.dev")
        # 2. 数据遍历，形成Examples对象
        examples = []
        for idx, word, label in self._read_ner_data(file_path):
            example = InputExample(guid="dev-{}".format(idx), text_a=word, label=label)
            examples.append(example)
        return examples

    def get_test_examples(self, data_dir):
        # 1. 组成文件路径
        file_path = os.path.join(data_dir, "example.min.test")
        # 2. 数据遍历，形成Examples对象
        examples = []
        for idx, word, label in self._read_ner_data(file_path):
            example = InputExample(guid="test-{}".format(idx), text_a=word, label=label)
            examples.append(example)
        return examples

    def get_labels(self):
        return ['PAD', 'CLS', 'SEP', 'O', 'B-ORG', 'I-ORG', 'B-PER', 'I-PER', 'B-LOC', 'I-LOC']


def convert_single_example(ex_index, example, label_list, max_seq_length,
                           tokenizer):
    """Converts a single `InputExample` into a single `InputFeatures`."""
    # 如果是填充样本，直接返回特殊值
    if isinstance(example, PaddingInputExample):
        return InputFeatures(
            input_ids=[0] * max_seq_length,
            input_mask=[0] * max_seq_length,
            segment_ids=[0] * max_seq_length,
            label_id=0,
            is_real_example=False)

    # 2. 构建标签名称和标签索引id之间的关系
    label_map = {}
    for (i, label) in enumerate(label_list):
        label_map[label] = i

    # 对文本进行单词划分的转换(中文是以字作为单词)
    tokens_a = example.text_a
    labels = example.label

    # 对文本进行截取的操作，如果超过最长长度，那么截取
    if len(tokens_a) > max_seq_length - 2:
        tokens_a = tokens_a[0:(max_seq_length - 2)]
        labels = labels[0:(max_seq_length - 2)]

    # 添加分隔符以及获取segment ids
    # The convention in BERT is:
    # (a) For sequence pairs:
    #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
    #  type_ids: 0     0  0    0    0     0       0 0     1  1  1  1   1 1
    # (b) For single sequences:
    #  tokens:   [CLS] the dog is hairy . [SEP]
    #  type_ids: 0     0   0   0  0     0 0
    #
    # Where "type_ids" are used to indicate whether this is the first
    # sequence or the second sequence. The embedding vectors for `type=0` and
    # `type=1` were learned during pre-training and are added to the wordpiece
    # embedding vector (and position vector). This is not *strictly* necessary
    # since the [SEP] token unambiguously separates the sequences, but it makes
    # it easier for the model to learn the concept of sequences.
    #
    # For classification tasks, the first vector (corresponding to [CLS]) is
    # used as the "sentence vector". Note that this only makes sense because
    # the entire model is fine-tuned.
    tokens = []
    segment_ids = []
    label_ids = []
    tokens.append("[CLS]")
    label_ids.append(label_map['CLS'])
    segment_ids.append(0)
    for token, label in zip(tokens_a, labels):
        tokens.append(token)
        segment_ids.append(0)
        label_ids.append(label_map[label])
    tokens.append("[SEP]")
    label_ids.append(label_map['SEP'])
    segment_ids.append(0)

    # 将token转换为id
    input_ids = tokenizer.convert_tokens_to_ids(tokens)

    # The mask has 1 for real tokens and 0 for padding tokens. Only real tokens are attended to.
    # 构建input mask: 实际值位置为1，填充值位置为0
    input_mask = [1] * len(input_ids)

    # 数据填充 Zero-pad up to the sequence length.
    while len(input_ids) < max_seq_length:
        input_ids.append(0)
        input_mask.append(0)
        segment_ids.append(0)
        label_ids.append(0)

    assert len(input_ids) == max_seq_length
    assert len(input_mask) == max_seq_length
    assert len(segment_ids) == max_seq_length
    assert len(label_ids) == max_seq_length

    if ex_index < 5:
        tf.logging.info("*** Example ***")
        tf.logging.info("guid: %s" % (example.guid))
        tf.logging.info("tokens: %s" % " ".join(
            [tokenization.printable_text(x) for x in tokens]))
        tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
        tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
        tf.logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
        tf.logging.info("label：%s " % " ".join([str(x) for x in example.label]))
        tf.logging.info("label：%s " % " ".join([str(x) for x in label_ids]))

    feature = InputFeatures(
        input_ids=input_ids,
        input_mask=input_mask,
        segment_ids=segment_ids,
        label_id=label_ids,
        is_real_example=True)
    return feature


def file_based_convert_examples_to_features(
        examples, label_list, max_seq_length, tokenizer, output_file):
    """Convert a set of `InputExample`s to a TFRecord file."""

    writer = tf.python_io.TFRecordWriter(output_file)

    for (ex_index, example) in enumerate(examples):
        if ex_index % 10000 == 0:
            tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))

        feature = convert_single_example(ex_index, example, label_list,
                                         max_seq_length, tokenizer)

        def create_int_feature(values):
            f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
            return f

        features = collections.OrderedDict()
        features["input_ids"] = create_int_feature(feature.input_ids)
        features["input_mask"] = create_int_feature(feature.input_mask)
        features["segment_ids"] = create_int_feature(feature.segment_ids)
        features["label_ids"] = create_int_feature(feature.label_id)
        features["is_real_example"] = create_int_feature(
            [int(feature.is_real_example)])

        tf_example = tf.train.Example(features=tf.train.Features(feature=features))
        writer.write(tf_example.SerializeToString())
    writer.close()


def file_based_input_fn_builder(input_file, seq_length, is_training,
                                drop_remainder):
    """Creates an `input_fn` closure to be passed to TPUEstimator."""

    name_to_features = {
        "input_ids": tf.FixedLenFeature([seq_length], tf.int64),
        "input_mask": tf.FixedLenFeature([seq_length], tf.int64),
        "segment_ids": tf.FixedLenFeature([seq_length], tf.int64),
        "label_ids": tf.FixedLenFeature([seq_length], tf.int64),
        "is_real_example": tf.FixedLenFeature([], tf.int64),
    }

    def _decode_record(record, name_to_features):
        """Decodes a record to a TensorFlow example."""
        example = tf.parse_single_example(record, name_to_features)

        # tf.Example only supports tf.int64, but the TPU only supports tf.int32.
        # So cast all int64 to int32.
        for name in list(example.keys()):
            t = example[name]
            if t.dtype == tf.int64:
                t = tf.to_int32(t)
            example[name] = t

        return example

    def input_fn(params, mode, config):
        """The actual input function."""
        batch_size = params["batch_size"]

        # For training, we want a lot of parallel reading and shuffling.
        # For eval, we want no shuffling and parallel reading doesn't matter.
        d = tf.data.TFRecordDataset(input_file)
        if is_training:
            d = d.repeat()
            d = d.shuffle(buffer_size=100)

        d = d.apply(
            tf.contrib.data.map_and_batch(
                lambda record: _decode_record(record, name_to_features),
                batch_size=batch_size,
                drop_remainder=drop_remainder))

        return d

    return input_fn


def _truncate_seq_pair(tokens_a, tokens_b, max_length):
    """Truncates a sequence pair in place to the maximum length."""

    # This is a simple heuristic which will always truncate the longer sequence
    # one token at a time. This makes more sense than truncating an equal percent
    # of tokens from each, since if one sequence is very short then each token
    # that's truncated likely contains more information than a longer sequence.
    while True:
        total_length = len(tokens_a) + len(tokens_b)
        if total_length <= max_length:
            break
        if len(tokens_a) > len(tokens_b):
            tokens_a.pop()
        else:
            tokens_b.pop()


def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
                 labels, num_labels, use_one_hot_embeddings,
                 num_filters=[128, 128, 128], region_sizes=[2, 3, 4]):
    # 1. 调用Bert的源码，构建bert模型
    model = modeling.BertModel(
        config=bert_config,  # Bert模型的配置参数
        is_training=is_training,  # 训练还是推理预测
        input_ids=input_ids,  # Tensor对象，输入的文本单词id， [N,T]
        input_mask=input_mask,  # Tensor对象，输入的文本mask信息， [N,T]，如果填充就为0，否则就为1
        token_type_ids=segment_ids,  # Tensor对象，输入的文本段落id， [N,T]
        use_one_hot_embeddings=use_one_hot_embeddings  # 词向量转换的方式
    )
    # # ========================================================
    # # 下列代码为原始的文本分类代码
    # # ========================================================
    # # In the demo, we are doing a simple classification task on the entire segment.
    # # If you want to use the token-level output, use model.get_sequence_output() instead.
    # # 得到Bert的输出
    # output_layer = model.get_pooled_output()
    #
    # # 基于Bert输出，结合业务编写其它的代码
    # hidden_size = output_layer.shape[-1].value
    # # w
    # output_weights = tf.get_variable(
    #     "output_weights", [num_labels, hidden_size],
    #     initializer=tf.truncated_normal_initializer(stddev=0.02))
    # # b
    # output_bias = tf.get_variable(
    #     "output_bias", [num_labels], initializer=tf.zeros_initializer())
    # with tf.variable_scope("loss"):
    #     if is_training:
    #         # I.e., 0.1 dropout
    #         output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)
    #     # logits = features * w + b
    #     logits = tf.matmul(output_layer, output_weights, transpose_b=True)
    #     logits = tf.nn.bias_add(logits, output_bias)
    #     probabilities = tf.nn.softmax(logits, axis=-1)
    #     log_probs = tf.nn.log_softmax(logits, axis=-1)
    #
    #     one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)
    #
    #     per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
    #     loss = tf.reduce_mean(per_example_loss)
    #
    #     return (loss, per_example_loss, logits, probabilities)

    # ========================================================
    # 下面代码为增加的/更改的代码
    # ========================================================
    """
    思路：提取Bert模型最后两层的输出，然后concat合并到一起，然后使用双向LSTM提取更高一层的特征信息，最终做CRF
    """
    # 1. 获取所有层的输出, 返回值是要给list列表
    encoder_outputs = model.get_all_encoder_layers()
    # 2. 获取最后四层的输出
    encoder_outputs = encoder_outputs[-2:]
    # 3. 将最后四层的Tensor对象合并到一起, [N,T,4*E], 每个批次N个样本，每个样本T个时刻，每个时刻有4*E大小维度的向量
    encoder_output = tf.concat(encoder_outputs, axis=-1)
    # 4. 基于输入信息计算序列长度信息
    lengths = tf.reduce_sum(tf.sign(tf.abs(input_ids)), axis=-1)
    # 4. 将其输入到LSTM+CRF中，提取对应的信息
    crf_model = text_rnn_crf.TextRNNCRF(
        network_name="TEXT_RNN_CRF",
        num_units=256,
        layers=1,  # 几层
        sequence_input_embedding=encoder_output,  # Bert提取出来的特征信息, [N,T,U]
        sequence_target_label=labels,  # 实际标签对象,[N,T]
        num_class=num_labels,  # 类别数目
        lengths=lengths,  # 序列长度信息
        keep_prob=0.8 if is_training else 1.0
    )
    return crf_model.total_loss, crf_model.per_example_loss, crf_model.predictions


def model_fn_builder(bert_config, num_labels, init_checkpoint, learning_rate,
                     num_train_steps, num_warmup_steps, use_tpu, use_one_hot_embeddings,
                     num_filters=[128, 128, 128], region_sizes=[2, 3, 4]
                     ):
    """Returns `model_fn` closure for TPUEstimator."""

    def model_fn(features, labels, mode, params):  # pylint: disable=unused-argument
        """
        TensorFlow内部会执行调用该函数
        :param features: 也就是基于TFRecord输出的数据，当前代码中是一个dict；也就是: file_based_input_fn_builder._decode_record的输出
        :param labels: 当前为None，实际标签再features内部
        :param mode: train、eval、predict
        :param params: 参数列表
        :return:
        """

        tf.logging.info("*** Features ***")
        for name in sorted(features.keys()):
            tf.logging.info("  name = %s, shape = %s" % (name, features[name].shape))

        # 1. 从加载的数据中获取对应的Tensor对象(原始信息)，NOTE: 格式信息和构建TFRecord以及解析TFRecord文件的代码相关
        input_ids = features["input_ids"]  # [batch_size, sequence_length]
        input_mask = features["input_mask"]  # [batch_size, sequence_length]
        segment_ids = features["segment_ids"]  # [batch_size, sequence_length]
        label_ids = features["label_ids"]  # [batch_size, sequence_length]
        is_real_example = None  # [batch_size]
        if "is_real_example" in features:
            is_real_example = tf.cast(features["is_real_example"], dtype=tf.float32)
        else:
            is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32)
        is_training = (mode == tf.estimator.ModeKeys.TRAIN)  # 判断是否是训练操作

        # 2. 构建模型（前向过程+损失函数的构建）
        # 返回值: 总的损失函数、每个样本的损失函数、预测id
        # [],[N,]、[N,T]
        (total_loss, per_example_loss, predictions) = create_model(
            bert_config,  # Bert模型配置信息
            is_training,  # 是否是训练操作
            input_ids,  # Tensor对象，模型的输入, [N,T]
            input_mask,  # Tensor对象，模型的输入, [N,T]
            segment_ids,  # Tensor对象，模型的输入, [N,T]
            label_ids,  # Tensor对象，模型希望输出, [N,]
            num_labels,  # 分类的标签数目
            use_one_hot_embeddings,  # embedding的实现方式：FC or embedding_lookup
            num_filters=num_filters,
            region_sizes=region_sizes
        )

        # 3. 对于bert模型的参数而言，使用给定的初始化路径进行参数初始化
        tvars = tf.trainable_variables()  # 获取所有的训练参数(Bert模型参数+业务模型参数)
        initialized_variable_names = {}
        scaffold_fn = None
        if init_checkpoint:
            # 加载具体哪些参数需要从bert模型中恢复(根据名称获取具体哪些需要加载恢复)
            (assignment_map, initialized_variable_names) = modeling.get_assignment_map_from_checkpoint(
                tvars,  # 所有参数列表
                init_checkpoint  # 给定的bert模型参数初始化路径
            )

            # 定义参数初始化的方式
            if use_tpu:

                def tpu_scaffold():
                    tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
                    return tf.train.Scaffold()

                scaffold_fn = tpu_scaffold
            else:
                # 初始化操作（延迟执行的，等到真正的初始化的时候才会执行） 参数assignment_map作用就是给定仅回复部分参数的值
                tf.train.init_from_checkpoint(init_checkpoint, assignment_map)

        # 日志打印
        tf.logging.info("**** Trainable Variables ****")
        for var in tvars:
            init_string = ""
            if var.name in initialized_variable_names:
                init_string = ", *INIT_FROM_CKPT*"
            tf.logging.info("  name = %s, shape = %s%s", var.name, var.shape, init_string)

        # 定义train、eval、predict具体的执行的输出对象
        output_spec = None
        if mode == tf.estimator.ModeKeys.TRAIN:
            # 优化器构建
            train_op = optimization.create_optimizer(
                total_loss,  # Tensor对象，[], 总损失
                learning_rate,  # 学习率
                num_train_steps,  # 总的训练批次数目
                num_warmup_steps,  # 学习率更改的批次阈值，当训练的批次小于该值的时候，学习率增大，当当前批次大于该值的时候，学习率减小
                use_tpu
            )

            # 固定写法，模型训练
            output_spec = tf.contrib.tpu.TPUEstimatorSpec(
                mode=mode,  # 给定操作对象为Train
                loss=total_loss,  # 给定损失函数
                train_op=train_op,  # 给定训练对象
                scaffold_fn=scaffold_fn)
        elif mode == tf.estimator.ModeKeys.EVAL:
            # 定义评估指标
            def metric_fn(per_example_loss, label_ids, predictions, is_real_example):
                predictions = tf.to_int32(predictions)
                accuracy = tf.metrics.accuracy(
                    labels=label_ids, predictions=predictions, weights=is_real_example)
                loss = tf.metrics.mean(values=per_example_loss, weights=is_real_example)
                return {
                    "eval_accuracy": accuracy,
                    "eval_loss": loss,
                }

            eval_metrics = (metric_fn,
                            [per_example_loss, label_ids, predictions, is_real_example])
            # 固定写法，模型评估
            output_spec = tf.contrib.tpu.TPUEstimatorSpec(
                mode=mode,
                loss=total_loss,
                eval_metrics=eval_metrics,  # 给定评估指标具体是什么
                scaffold_fn=scaffold_fn)
        else:
            # 定义模型预测输出信息
            output_spec = tf.contrib.tpu.TPUEstimatorSpec(
                mode=mode,
                predictions={
                    "predictions": predictions  # [N,T]
                },  # 给定最终预测输出值是什么，给定一个字典即可
                scaffold_fn=scaffold_fn)
        return output_spec

    return model_fn


def main(_):
    tf.logging.set_verbosity(tf.logging.INFO)

    # 参数校验，对给定的预训练好的Bert模型进行校验
    tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
                                                  FLAGS.init_checkpoint)

    # 参数校验，至少要求train、eval以及predict三个操作中，执行任意一个操作
    if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict:
        raise ValueError(
            "At least one of `do_train`, `do_eval` or `do_predict' must be True.")

    # 加载Bert的配置信息，从给定的磁盘路径中
    bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)

    # 命令行给定的最长序列大小不能够超过预训练好的序列长度大小(Bert预训练的模型是在长度为512的情况下训练的)
    if FLAGS.max_seq_length > bert_config.max_position_embeddings:
        raise ValueError(
            "Cannot use sequence length %d because the BERT model "
            "was only trained up to sequence length %d" %
            (FLAGS.max_seq_length, bert_config.max_position_embeddings))

    # 模型最终的数据输出文件夹路径的构建
    tf.gfile.MakeDirs(FLAGS.output_dir)

    # 定义任务对应的数据加载对象，以task_name为key，以该task任务对应的数据加载方式作为Value
    processors = {
        "ner": NERProcessor
    }

    # 获取任务名称
    task_name = FLAGS.task_name.lower()

    # 判断当前模型是否支持该任务，如果不支持，那么直接报错
    if task_name not in processors:
        raise ValueError("Task not found: %s" % (task_name))

    # 获取该任务对应的数据加载器
    processor = processors[task_name]()

    # 根据数据加载器获取对应的标签列表，是一个list集合，内部为标签的字符串
    label_list = processor.get_labels()

    # 基于字典文件构建数据token处理对象（分词、词转id、id转词等功能，中文中以字作为词）
    tokenizer = tokenization.FullTokenizer(
        vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)

    # TPU运行的参数信息，如果你的运行环境是GPU或者CPU，那么TPU的配置不影响你的运行
    tpu_cluster_resolver = None
    if FLAGS.use_tpu and FLAGS.tpu_name:
        tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
            FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
    is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2

    # 运行配置信息的给定
    run_config = tf.contrib.tpu.RunConfig(
        cluster=tpu_cluster_resolver,  # 给定TPU的运行设备
        master=FLAGS.master,  # master信息
        model_dir=FLAGS.output_dir,  # 模型输出路径
        save_checkpoints_steps=FLAGS.save_checkpoints_steps,  # 模型持久化的间隔批次
        tpu_config=tf.contrib.tpu.TPUConfig(
            iterations_per_loop=FLAGS.iterations_per_loop,
            num_shards=FLAGS.num_tpu_cores,
            per_host_input_for_training=is_per_host))

    # 训练数据、最多允许训练的批次数目、学习率变化的步长的阈值(在这个值之前，学习率是增大的，在这个批次之后，学习率减小)
    train_examples = None
    num_train_steps = None
    num_warmup_steps = None  # 学习率的变化类似YOLOv3中讲的那种学习率变化方式，先学习率增大，然后再减小
    if FLAGS.do_train:
        train_examples = processor.get_train_examples(FLAGS.data_dir)
        num_train_steps = int(
            len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
        num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)

    # 核心，也是我们如果要修改，主要修改的地方；构建一个函数，该函数model_fn的功能是构建模型
    model_fn = model_fn_builder(
        bert_config=bert_config,  # Bert模型的配置信息
        num_labels=len(label_list),  # 分类模型的标签数目
        init_checkpoint=FLAGS.init_checkpoint,  # Bert模型初始化的路径
        learning_rate=FLAGS.learning_rate,  # 学习率
        num_train_steps=num_train_steps,  # 总的训练批次
        num_warmup_steps=num_warmup_steps,  # 学习率变化的批次阈值(当前批次小于该值，学习率是增大，大于该值，学习率是减小)
        use_tpu=FLAGS.use_tpu,  # 是否使用TPU
        use_one_hot_embeddings=FLAGS.use_tpu  # 实现Embedding的时候是否使用全连接的方式(FC or embedding_lookup)
    )

    # If TPU is not available, this will fall back to normal Estimator on CPU r GPU.
    # 构建一个封装好的算法对象
    estimator = tf.contrib.tpu.TPUEstimator(
        use_tpu=FLAGS.use_tpu,  # 是否使用TPU
        model_fn=model_fn,  # 模型构建函数
        config=run_config,  # 运行配置信息
        train_batch_size=FLAGS.train_batch_size,  # 训练的批次大小
        eval_batch_size=FLAGS.eval_batch_size,  # 验证的批次大小
        predict_batch_size=FLAGS.predict_batch_size  # 数据预测的批次大小
    )

    # 进行训练操作
    if FLAGS.do_train:
        # 1. 将训练数据转换为TFRecord格式的数据保存到磁盘(InputExample -> InputFeatures -> TFRecord)
        train_file = os.path.join(FLAGS.output_dir, "train.tf_record")
        file_based_convert_examples_to_features(
            train_examples,  # 训练数据，List列表，内部为InputExample对象
            label_list,  # 标签数据，List列表，内部为具体的类别字符串
            FLAGS.max_seq_length,  # 当前模型训练允许的最长序列长度，当序列实际长度超过该值的时候，进行截断，小于该值，进行填充
            tokenizer,  # 主要应用：文本转单词以及单词转id
            train_file  # TFRecord数据保存的磁盘路径
        )
        tf.logging.info("***** Running training *****")
        tf.logging.info("  Num examples = %d", len(train_examples))
        tf.logging.info("  Batch size = %d", FLAGS.train_batch_size)
        tf.logging.info("  Num steps = %d", num_train_steps)

        # 2. 基于TFRecord的数据文件构建数据读入的解析函数
        train_input_fn = file_based_input_fn_builder(
            input_file=train_file,
            seq_length=FLAGS.max_seq_length,
            is_training=True,
            drop_remainder=True
        )

        # 3. 模型训练
        estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)

    # 进行验证/评估操作
    if FLAGS.do_eval:
        # 1. 获取验证数据集， List列表，内部为InputExample对象
        eval_examples = processor.get_dev_examples(FLAGS.data_dir)

        # 2. 得到实际的样本数目
        num_actual_eval_examples = len(eval_examples)

        # 3. 如果是TPU运行，进行必要的数据样本填充(填充PaddingInputExample对象)
        if FLAGS.use_tpu:
            # TPU requires a fixed batch size for all batches, therefore the number
            # of examples must be a multiple of the batch size, or else examples
            # will get dropped. So we pad with fake examples which are ignored
            # later on. These do NOT count towards the metric (all tf.metrics
            # support a per-instance weight, and these get a weight of 0.0).
            while len(eval_examples) % FLAGS.eval_batch_size != 0:
                eval_examples.append(PaddingInputExample())

        # 4. 将验证数据转换为TFRecord格式的数据保存到磁盘(InputExample -> InputFeatures -> TFRecord)
        eval_file = os.path.join(FLAGS.output_dir, "eval.tf_record")
        file_based_convert_examples_to_features(
            eval_examples,  # 验证数据，List列表，内部为InputExample对象
            label_list,  # 标签数据，List列表，内部为具体的类别字符串
            FLAGS.max_seq_length,  # 当前模型训练允许的最长序列长度，当序列实际长度超过该值的时候，进行截断，小于该值，进行填充
            tokenizer,  # 主要应用：文本转单词以及单词转id
            eval_file  # TFRecord数据保存的磁盘路径
        )
        tf.logging.info("***** Running evaluation *****")
        tf.logging.info("  Num examples = %d (%d actual, %d padding)",
                        len(eval_examples), num_actual_eval_examples,
                        len(eval_examples) - num_actual_eval_examples)
        tf.logging.info("  Batch size = %d", FLAGS.eval_batch_size)

        # 5. 计算验证的步长数目(批次的数目)
        # This tells the estimator to run through the entire set.
        eval_steps = None
        # However, if running eval on the TPU, you will need to specify the
        # number of steps.
        if FLAGS.use_tpu:
            assert len(eval_examples) % FLAGS.eval_batch_size == 0
            eval_steps = int(len(eval_examples) // FLAGS.eval_batch_size)

        # 6. 数据加载函数的构建
        eval_drop_remainder = True if FLAGS.use_tpu else False
        eval_input_fn = file_based_input_fn_builder(
            input_file=eval_file,
            seq_length=FLAGS.max_seq_length,
            is_training=False,
            drop_remainder=eval_drop_remainder
        )

        # 7. 模型校验，得到校验结果
        result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps)

        # 8. 验证结果数据的获取&保存
        output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
        with tf.gfile.GFile(output_eval_file, "w") as writer:
            tf.logging.info("***** Eval results *****")
            for key in sorted(result.keys()):
                tf.logging.info("  %s = %s", key, str(result[key]))
                writer.write("%s = %s\n" % (key, str(result[key])))

    # 进行预测操作
    if FLAGS.do_predict:
        # 1. 获取需要验证的数据集， List列表，内部为InputExample对象
        predict_examples = processor.get_test_examples(FLAGS.data_dir)

        # 2. 得到实际的样本数目
        num_actual_predict_examples = len(predict_examples)

        # 3. 如果是TPU运行，进行必要的数据样本填充(填充PaddingInputExample对象)
        if FLAGS.use_tpu:
            # TPU requires a fixed batch size for all batches, therefore the number
            # of examples must be a multiple of the batch size, or else examples
            # will get dropped. So we pad with fake examples which are ignored
            # later on.
            while len(predict_examples) % FLAGS.predict_batch_size != 0:
                predict_examples.append(PaddingInputExample())

        # 4. 将预测数据转换为TFRecord格式的数据保存到磁盘(InputExample -> InputFeatures -> TFRecord)
        predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record")
        file_based_convert_examples_to_features(
            predict_examples,  # 预测数据，List列表，内部为InputExample对象
            label_list,  # 标签数据，List列表，内部为具体的类别字符串
            FLAGS.max_seq_length,  # 当前模型训练允许的最长序列长度，当序列实际长度超过该值的时候，进行截断，小于该值，进行填充
            tokenizer,  # 主要应用：文本转单词以及单词转id
            predict_file  # TFRecord数据保存的磁盘路径
        )
        tf.logging.info("***** Running prediction*****")
        tf.logging.info("  Num examples = %d (%d actual, %d padding)",
                        len(predict_examples), num_actual_predict_examples,
                        len(predict_examples) - num_actual_predict_examples)
        tf.logging.info("  Batch size = %d", FLAGS.predict_batch_size)

        # 5. 数据加载函数的构建
        predict_drop_remainder = True if FLAGS.use_tpu else False
        predict_input_fn = file_based_input_fn_builder(
            input_file=predict_file,
            seq_length=FLAGS.max_seq_length,
            is_training=False,
            drop_remainder=predict_drop_remainder)

        # 6. 预测数据，得到预测结果
        result = estimator.predict(input_fn=predict_input_fn)

        # 7. 预测结果处理并输出
        output_predict_file = os.path.join(FLAGS.output_dir, "test_results.tsv")
        with tf.gfile.GFile(output_predict_file, "w") as writer:
            num_written_lines = 0
            tf.logging.info("***** Predict results *****")
            for (i, prediction) in enumerate(result):
                # ====================================
                # 下列代码是直接输出预测值
                # ====================================
                prediction_id = prediction['predictions']  # 当前样本的预测类别id，[T,]
                prediction_label = [label_list[id] for id in prediction_id]  # 获取对应的类别名称
                output_line = "%s\n" % "\t".join(prediction_label)  # 先输出类别名称，再输出概率值
                writer.write(output_line)
                num_written_lines += 1
        assert num_written_lines == num_actual_predict_examples


if __name__ == "__main__":
    flags.mark_flag_as_required("data_dir")
    flags.mark_flag_as_required("task_name")
    flags.mark_flag_as_required("vocab_file")
    flags.mark_flag_as_required("bert_config_file")
    flags.mark_flag_as_required("output_dir")
    tf.app.run()
